Table 1.

Characteristics of included studies.

Author/Year	Study description	Country of origin	Participants (n)	Key findings
Ventilator allocation
Asghari (2021)40	Online survey; 11 allocation statements; respondents agreed/disagreed with statements.	Iran	1262	Priority based on survival probability, quality of life & social usefulness. Little agreement with prioritization based on first come, first served.
Huang (2020)41	Online survey. Two-stage experimental design (respondents assigned to conditions with/without veil of ignorance applied).	USA	1276	Veil of ignorance (VOI) reasoningb favours allocating scarce ventilators to younger patients over older patients, showing that when engaged in VOI reasoning, respondents are more likely to approve of allocation that aims to saving the most lives.
Huseynov (2020)42	Online survey; 1 hypothetical scenario: allocation of 100 ventilators among 1000 COVID-19 patients of varying ages.	USA	586	Priority based on survival probability (younger patients). Preference for treating own age group equally.
Jin (2021)43	Online choice based conjoint design; 15 choice sets; 2 hypothetical patients. Recruitment across 11 countries (USA, Brazil, India, UK, Italy, Germany, France, Australia, Spain, China and South Korea)	USA	5175	Priority based on survival probability (i.e. allocation to younger patients).
Norman (2021)44	Online DCEa; 12 choice sets.	Australia	1050	Priority based on survival probability (i.e. younger, non-smokers), social usefulness & without disability.
Werner & Landau (2020)45	Online survey; 3 hypothetical patients with/without Alzheimer's Disease. Respondents allocated ventilator by order (1st, 2nd and last).	Israel	309	Priority based on survival probability & quality of life. Least priority is given to oldest patient with cognitive disorder.
Wilkinson (2020)46	Online survey; 38 choices: 2 hypothetical patients.	UK	768	Priority based on survival probability, quality of life & social usefulness. Support for reallocating treatment to save more lives
Intensive care bed (ICU) allocation
Fallucchi (2020)47	Online survey; 8 hypothetical triage statements: 2 patients.	USA	1033	Priority based on survival probability, social usefulness & those infected with COVID-19. Support for reallocation only when patient has received treatment for 2 months.
Street (2021)48	Online DCEa; 7 choice sets; 14 patient pairs. Respondents prioritise care between two patients requiring ICU bed.	Portugal	306	Priority given to patients based on their prognosis (e.g. younger) and social usefulness (i.e. healthcare workers, caregivers).
Ventilator and intensive care bed (ICU) allocation
Pinho (2021)39	Online survey; 6 hypothetical allocation statements; 2 patients of different ages, professions, symptom severity, survival.	Australia	306	Priority given to patients based on their prognosis, followed by severity of health condition and age. When confronted with survival, youngest first was preferred. Egalitarian allocation least preferred.
Sprengholz (2022)49	Online survey to investigate public's prioritisation preference toward ICU admission for patients who differed in health condition, expected treatment benefits and COVID-19 vaccination status.	Germany	1014	Priority given to treating (1) patients who are vaccinated over non-vaccinated; (2) patients with serious health conditions (e.g. heart attack) over patients with COVID-19. The public also more likely to admit a patient to ICU when this meant withholding rather than withdrawing care from another patient.
Generic triage policy allocation
Buckwalter & Peterson (2020)50	Three online experiments to investigate public attitude toward hypothetical triage allocation statements.	USA	1868	Priority based on survival probability & seriousness of condition, but not when entail reallocation between existing patients, or when they disadvantage at risk groups.
COVID-19 vaccine allocation
Gollust (2019)49	Online & telephone survey to assign preference (high-med-low) for delivery of COVID-19 vaccination; 8 hypothetical population groups.	USA	586	Priority to people with lower age, higher risk of dying from COVID-19; are pregnant, medical workers or non-medical essential workers.
Luyten (2020)51	Online survey to assign preference (most appropriate-least appropriate) for delivery of COVID-19 vaccination (8 hypothetical population groups).	Belgium	2060	Priority to people who are: essential workers, chronically ill and older. Least preferred were egalitarian strategies (e.g. lottery, first come, first served).
Sprengholz (2021)51	Online survey to examine public opinion toward: (1) government COVID-19 allocation policy objectives; and (2) allocating vaccine priority to certain groups (e.g. older vs younger, workers with high exposure risk, nursing home residents).	Germany	1379	Public support official COVID-19 vaccination policy objectives. Public support giving vaccine priority to workers with high exposure risk. Least support for assigning priority to older individuals and those living in nursing homes.

^aDCE = discrete choice experiment.

^bVeil of ignorance reasoning = is designed to elicit impartial decision making by denying respondents potentially biasing information about who will benefit the most or least from the available options.